[en] Numerous bibliographic reviews related to the use of AI for the behavioral detection of farm animals exist, but they only focus on a particular type of animal. We believe that some techniques were used for some animals that could also be used for other types of animals. The application and comparison of these techniques between animal species are rarely done. In this paper, we propose a review of machine learning approaches used for the detection of farm animals’ behaviors such as lameness, grazing, rumination, and so on. The originality of this paper is matched classification in the midst of sensors and algorithms used for each animal category. First, we highlight the most implemented approaches for different categories of animals (cows, sheep, goats, pigs, horses, and chickens) to inspire researchers interested to conduct investigation and employ the methods we have evaluated and the results we have obtained in this study. Second, we describe the current trends in terms of technological development and new paradigms that will impact the AI research. Finally, we critically analyze what is done and we draw new pathways of research to
advance our understanding of animal’s behaviors.
Disciplines :
Computer science
Author, co-author :
Debauche, Olivier ; Université de Liège - ULiège > TERRA Research Centre
Elmoulat, Meryem
Mahmoudi, Saïd
Bindelle, Jérôme ; Université de Liège - ULiège > Département GxABT > Ingénierie des productions animales et nutrition
Lebeau, Frédéric ; Université de Liège - ULiège > Département GxABT > Biosystems Dynamics and Exchanges
Language :
English
Title :
Farm Animals’ Behaviors and Welfare Analysis with AI Algorithms: A Review
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